RMBG v1.4 is our state-of-the-art background removal model, designed to effectively separate foreground from background in a range of categories and image types. This model has been trained on a carefully selected dataset, which includes: general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale. The accuracy, efficiency, and versatility currently rival leading source-available models. It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount. Developed by BRIA AI, RMBG v1.4 is available as a source-available model for non-commercial use. To purchase a…
This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (CAAI AIR 2024). Visit our GitHub repo: https://github.com/ZhengPeng7/BiRefNet for more details -- codes, docs, and model zoo!
Parameters44M
Context—
Weights177.6 MB
Licensemit
AccessOpen weights
Monthly Downloads148.5k
Runs On
What it takes to serve BiRefNet_lite (44M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
By Peng Zheng, published under mit, revision aa62cd87eafb.
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
Peng Zheng 1,4,5,6,
Dehong Gao 2,
Deng-Ping Fan 1*,
Li Liu 3,
Jorma Laaksonen 4,
Wanli Ouyang 5,
Nicu Sebe 6
1 Nankai University 2 Northwestern Polytechnical University 3 National University of Defense Technology 4 Aalto University 5 Shanghai AI Laboratory 6 University of Trento
| DIS-Sample_1 | DIS-Sample_2 |
|---|---|
This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (CAAI AIR 2024).
Visit our GitHub repo: https://github.com/ZhengPeng7/BiRefNet for more details -- codes, docs, and model zoo!
How to use (this tiny version)
0. Install Packages:
pip install -qr https://raw.githubusercontent.com/ZhengPeng7/BiRefNet/main/requirements.txt
1. Load BiRefNet:
Use codes + weights from HuggingFace
Only use the weights on HuggingFace -- Pro: No need to download BiRefNet codes manually; Con: Codes on HuggingFace might not be latest version (I'll try to keep them always latest).
Configuration
- Architecture
- BiRefNet
Identity and Version
- Repository
- ZhengPeng7/BiRefNet_lite
- Publisher
- Peng Zheng
- Task
- Image segmentation
- Modality
- Image
- Library
- birefnet
- Parameters
- 44M parameters
- Languages
- Not stated by the source
- Revision
- aa62cd87eafb9cc43056d08ef3615a14628b831d
- First published
- 2024-08-02
- Last updated
- 2026-08-29
Files and Weights
8 files, 177.7 MB in total. The weights are 1 file totalling 177.6 MB in safetensors.
Weights1 file · 177.6 MB
Configuration4 files · 97.6 KB
Documentation1 file · 8.7 KB
Other1 file · 149 B
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 177.6 MB | 4417d8979525 |
| BiRefNet_config.py | Configuration | 298 B | — |
| birefnet.py | Configuration | 92.1 KB | — |
| config.json | Configuration | 410 B | — |
| handler.py | Configuration | 4.7 KB | — |
| README.md | Documentation | 8.7 KB | — |
| requirements.txt | Other | 149 B | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 177.6 MB
Download from Peng Zheng
Released by Peng Zheng through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2401.03407
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 177.6 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Built on This Model
- Quantized frombirefnet-lite-512
- Derived frombirefnet-lite-512
Questions About BiRefNet_lite
How much GPU memory does BiRefNet_lite need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (44M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run BiRefNet_lite on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use BiRefNet_lite commercially?
Yes. BiRefNet_lite is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
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